Mental fatigue detection from a brain computer interface(BCI) - Soh, Kok Weng (2018)
Driving fatigue poses a dangerous threat to road safety due to the decline in the attention and reaction time of drivers. Thus, the development of a fatigue monitoring and feedback system for accident prevention has been a major focus in the field of driving safety. The system requires constant monitoring and estimate of the alertness of the driver. The aim of the project was to develop a new real-time electroencephalogram(EEG) based driving fatigue detection system via the usage of SVM. In order to gather data required for the training of the Support Vector Machines(SVM) an experiment was conducted which required the subjects to complete a 40 minute driving simulator task. A total of 48 features were computed from 4 different EEG channels to differentiate the alert and fatigued states. The features were then fed to a SVM classifier which yielded a raw and smoothed accuracy of 68.8% and 77%, respectively. The results indicated that the parameters can differentiate both states accurately. Thus, they were used to develop a new real-time EEG based driving fatigue detection system.